Angiographic methods, which generate images of blood vessels, are of great importance in the assessment of vascular diseases, such as atherosclerosis. They can provide information on vessel morphology and function which aids clinicians with diagnosis, prognosis and treatment planning in these patients. Vessel-selective angiography provides additional information about the relative importance of each feeding artery which can be useful in a number of areas, such as the assessment of collateral blood flow or the evaluation of blood supply to a tumor or arteriovenous malformation. However, many angiographic methods provide only qualitative information on blood flow, making objective comparisons between vessels and across subjects difficult. In addition, many of these methods have a number of other drawbacks such as the requirement for an invasive procedure, use of ionizing radiation or the administration of a contrast agent.
Phase-contrast magnetic resonance angiography (MRA) (e.g., Dumoulin and Hart, 1986) attempts to provide quantitative blood velocity and flow rate measurements, but it could not be easily combined with a vessel-encoded or vessel-selective preparation to give vessel-specific information about blood flow in downstream vessels. Further, it is hampered by long scan times (Miyazaki et al., 2008) and insensitivity to slow flowing blood. Previous methods for quantification of arterial spin labeling (ASL) dynamic angiography (van Osch et al. 2006) have been limited to pulsed ASL techniques which are less Signal-to-Noise Ratio (SNR) efficient than continuous or pseudo-continuous approaches, require additional calibration scans to be carried out, and make the assumption of plug flow through the vessels.
In addition to blood flow quantification, the ability to visualize blood flow in a vessel-selective manner is of importance in a range of cerebrovascular diseases. For example, in patients with steno-occlusive disease, this information reveals the extent of collateral blood flow, which is important for maintaining the viability of brain tissue when the primary feeding artery is compromised (Liebeskind, 2003). Clinically, such assessments are generally performed using x-ray digital subtraction angiography (DSA) which provides excellent temporal and spatial resolution, but is limited by its invasive nature, the use of ionizing radiation and the requirement for catheter repositioning to assess multiple arteries. In addition, these procedures carry a risk of contrast agent reaction, silent ischaemia (Bendszus et al., 1999), or even stroke (Kaufmann et al., 2007). Other angiographic methods, such as conventional time-of-flight (TOF) magnetic resonance angiography (Masaryk et al., 1989), lack hemodynamic and vessel-selective information.
Non-invasive vessel-selective perfusion mapping methods, such as those based on arterial spin labeling (ASL) (Hendrikse et al., 2004; Werner et al., 2005; Gunther, 2006; Wong, 2007; Dai et al., 2010; and Helle et al., 2010), can be quantitative, allow the inference of abnormal flow patterns and have been shown to correlate well with x-ray DSA for the assessment of collateral flow (Chng et al., 2008; and Wu et al., 2008). However, the vessel morphology and flow patterns are not visualized directly, and in patients with very delayed blood arrival T1 decay may lead to significant signal attenuation before the blood reaches the tissue.
Recently, we proposed a method for vessel-selective dynamic angiography (Okell et al., 2010) by combining a vessel-encoded pseudo-continuous arterial spin labeling (VEPCASL) preparation (Wong, 2007) with a 2D thick-slab flow-compensated segmented Fast Low Angle Shot (FLASH) readout (Gunther et al., 2002; and Sallustio et al., 2008). This technique requires no catheter insertion or contrast agent and provides useful qualitative information on the morphological and functional status of each artery. However, it is hampered by the lack of quantitative information. In particular, late arriving blood will appear less intense due to the greater T1 decay which may bias the observer's interpretation of the data. In addition, in order to optimize the signal-to-noise ratio (SNR) a long labeling duration is generally used, so the first acquired image corresponds to a time at which most of the vessels are filled with labeled blood and thus only outflow of the bolus can be observed.
It is therefore desirable to develop a system and method for quantification of blood volume flow rate in specific vessel segments using dynamic angiographic data that can also account for attenuation of the measured signal.
A simple modification of perfusion quantification methods (such as the general ASL kinetic model [Buxton et al., 2008]) are not appropriate since they assume accumulation of the contrast agent within each voxel, which does not occur in large vessels. Indeed, the signal strength measured in these experiments depends mainly on blood volume, not blood flow. It is the total volume of labeled blood created by labeling for a specific time which is crucial here. The attenuation of the signal amplitude must also be accounted for.
Accordingly, there is a need to address the aforementioned deficiencies and inadequacies.